Text Generation
Transformers
Safetensors
English
testgeniy
causal-lm
reasoning
mathematics
logic
long-context
4k-context
small-language-model
Instructions to use Asilarkness/testgeniy with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Asilarkness/testgeniy with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="Asilarkness/testgeniy")# Load model directly from transformers import AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained("Asilarkness/testgeniy", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use Asilarkness/testgeniy with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Asilarkness/testgeniy" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Asilarkness/testgeniy", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/Asilarkness/testgeniy
- SGLang
How to use Asilarkness/testgeniy with SGLang:
Install from pip and serve model
# Install SGLang from pip: pip install sglang # Start the SGLang server: python3 -m sglang.launch_server \ --model-path "Asilarkness/testgeniy" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Asilarkness/testgeniy", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker images
docker run --gpus all \ --shm-size 32g \ -p 30000:30000 \ -v ~/.cache/huggingface:/root/.cache/huggingface \ --env "HF_TOKEN=<secret>" \ --ipc=host \ lmsysorg/sglang:latest \ python3 -m sglang.launch_server \ --model-path "Asilarkness/testgeniy" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Asilarkness/testgeniy", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use Asilarkness/testgeniy with Docker Model Runner:
docker model run hf.co/Asilarkness/testgeniy
Add reasoning SFT v3 data manifest
Browse files
candidates/budgie-alignment-v2/reasoning-sft-then-rl-v3/bootstrap/v3_reasoning_sft_manifest.json
ADDED
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{
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"scanned": 5251,
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"verified_pool": 1600,
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"train": 800,
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"dev": 208,
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"difficulty_train": {
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"medium": 511,
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"hard": 289
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},
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"sources_train": {
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"olympiads": 447,
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"cn_contest": 191,
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"aops_forum": 285,
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"number_theory": 7,
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"amc_aime": 36,
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"inequalities": 21,
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"olympiads_ref": 13
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},
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"problem_types_train": {
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"Geometry": 236,
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"Inequalities": 30,
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"Combinatorics": 158,
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"Algebra": 277,
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"Number Theory": 218,
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"Logic and Puzzles": 44,
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"Calculus": 22,
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"Other": 15
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},
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"rejections": {
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"verify": 2910,
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"length": 740
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},
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"benchmark_family_rows": 0,
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"decontamination": "normalized exact + shared fixed-gate 12-token windows + source bans",
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"verification": "math_verify(reference solution, answer)",
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"seconds": 69.16955494880676,
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"difficulty_dev": {
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"hard": 78,
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"medium": 130
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}
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}
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